1. The purpose of this webpage ...is to explain how network
models can
be used to describe
development projects. Descriptions can be useful for documenting an intended set of
activities and outcomes, and for documenting the actual activities
and the outcomes that eventuate. An evaluation will normally need
to develop and compare both types of description.

Also relevant:"Scale, Complexity and the
Representation of Theories of Change Part I and Part II", in Evaluation,
Vol
10(1):101-121. (2004) and Vol 11(2):133-149, (2005),
Sage Publications, London. Request
a copy

Working
with the Logical Framework, a section of the MandE
NEWS website. Network models can provide a complement to, and
an alternative to, Logical Framework descriptions of development
projects.

2. Where are network models
likely to be a useful means of description? (return to Contents)

Network models are likely to be useful in any of the following
kinds of settings:

Where there are many actors (people and / or
organisations) who are fairly autonomous and where there is no central
authority able to direct the behavior of all the other actors.

In large projects with many stakeholders,
rather than
small
projects with few, where a single authority is less likely to be found.
National and international level development activities are
likely
to have larger numbers of actors, without there being one over-riding
authority.

In projects where there is no single objective,
but
many
alternative and/or competing objectives. These may be symptomatic of
the absence of a central authority, and / or the intention that the
participating parties have considerable independence.

In projects where a given output may be used by
many
actors and a given actor may use many outputs. In other words, where
there is a complex web of relationships, not simple one-to-one
connections.

In projects deliberately designed to function
as
networks. These will vary in the extent that they are designed to be
centrally managed ( /coordinated / facilitated) or not.

Network models can also be useful as a means of describing projects
that are more "traditional", where there is a single agreed objective,
clear lines of authority, clearly differentiated responsibilities, and
which can be describe by a temporal logical model (e.g. a
Logical
Framework). Here network models can provide supplementary detail that
cannot be easily represented otherwise. See section 5 below for more on
this topic.

A network is a collection of people and
/ or
things that are connected to each other by some kind of relationship.
Many kinds of entities can be part of a network: people, projects,
documents, events, organisations, cities, countries, etc. And there are
many kinds of relationships that can link such entities, involving
transmission or exchange of information, money, goods, affection,
influence, infection, etc.

Note:
A network does not need to be labelled or formally named as a
network to be a network. Such networks are part of a much
larger set of
networks, some of which may be recognised as de facto or
informal networks, and many others may not be.

Note: Because this
webpage is concerned with the use of network models for planning and
evaluation purposes the focus will be on networks of actors,
objects and events that are observable.
That can be interviewed or that can be read, or that can be read about.
So network models of abstract processes will not be discussed here.
(PS: Here is an example of a
network of abstract processes that was part of the ToR for an
evaluation of a complex "project" (multi-donor budget support). The
challenge with this network would be to establish linkages between
events which, as
described in this example, are themselves not readily observable)

Network
analysis is the analysis of the structure of
relationships within a network. Social
Network Analysis (SNA) is especially relevant to the
development of network models of development aid programs, because
development aid is about people and their institutions. It is the main
intellectual influence on the contents of this webpage. Network
analysis is also done of biological systems, physical systems and
economies, but those usages are not discussed here.

A Logical Framework
is kind of temporal
logic model, that describes development projects in
terms of a chain of "if we do this...and this ...assumption holds, then
this ....will happen, and if this happens..." statements

There are two reasons for looking at the
relationship between temporal logic models and network models.
Firstly, for readers who are more familiar with the use of the Logical
Framework as a means of describing a project, it may be useful to use
the Logical Framework as a starting point for developing a simple
example network model, which can then be elaborated into more
detail. Secondly, inter-operability.
There are many circumstances where a Logical Framework description
of a project will be required by donors and or senior management, and
many situations where Logical Framework descriptions are not sufficient
to capture and help manage the complexity of a project. It should
therefore be possible to convert a Logical Framework
description of project into a Network Model of a project, and convert
a Network Model of a project into a Logical Framework description of
project.

As argued on the Logical
Framework page on this website, a Logical Framework
description of a development project can be improved upon by making it
more
"actor oriented". That is, by clearly identifying who is involved at
each level of the Logical Framework's description of a project. Then by
identifying how these actors are expected to interact with each other.
Doing this makes it easier for everyone to understand the overall
storyline that
should be connecting Activities to Outputs to Purpose to Goal. Doing
this also helps make it possible to develop a network model of the same
project design, as will be shown further below.

Here is a very simple actor-oriented
interpretation of a
Logical Framework, representing an imaginary development project that
can be made more complex, and realistic, later on. Read from the bottom
of the table upwards.

The Actors
involved

And how they relate to the levels
of a Logical Framework

Other
village members

Who
interact with the VDCs, and this leads to changes in their lives, in
the longer term. (Goal level outcomes / impact)

Village
Development Committees (VDCs)

Use
the goods or services provided by the NGO (Outputs) and this
results in medium term changes (Purpose level outcomes)

NGO

Undertake various Activities, some of which
generate some Outputs:
goods and services that are usable by the VDCs

Donors

Provide Inputs
to the NGO (these are
sometimes listed in OVI column next to Activities)

In the Logical Framework the Assumptions column
also has
an important role to play, especially at the Output and Purpose level.
Assumptions, statements here usually relate to external influences that
could effect
the linkage between events at Output and Purpose level, and between the
Purpose and Goal level. While often described as abstract processes ,
it is possible and useful to describe these Assumptions in more
actor-oriented terms, by identifying specific organisations, groups or
individuals who could have a positive or negative influence on the
actors in the main narrative column (as in the table above). Doing so
brings us a further step closer to a network view of development
projects. See more on this below...

Temporal logic models, such as the Logical Framework, vary in the
number of levels or stages they have within their model. While the
Logical Framework has four, there is no reason why an actor oriented
interpretation of the Logical Framework could not have more, if the
chain of actors linking the project managers with the final intended
beneficiaries was longer.

6. Two
complimentary ways of representing networks: diagrams and matrices (return to Contents)

In Social Network Analysis (SNA) a network can be represented in matrix form and in
the form of a network
diagram. A network diagram can be converted into
a matrix, and vice versa. The network diagram and network matrix shown
below both represent the relationships between the actors involved in
the table above. Note that the convention with such matrices is that
the cell entries always show the relationship that exist from the row actor to the column
actor. In this example the relationships between all the actors in the
network are two way, so the matrix is symmetrical. But this will not be
the case if the focus is on funding relationships or on influence
processes, for example (see more on this below).

Network diagrams are good for providing a overview of what is
happening. Network matrices are good for providing detail that can be
systematically analysed. More on this below.

Top-down: Start with
broad categories of actors and map out the expected linkages between
them. Then break these categories down into smaller categories of
actors. Then map
the relationships between them, and with others. This can be done using
network diagrams to start with, or by matrices

The
network
model shown above is
clearly a very simple view of a
development project. It is a useful starting point, but needs more
detail if it is to be of operational use for planning and evaluation
purposes.
Looking at each of the cells in the matrix above, it is easy to see
that each cell could be developed into a matrix of its own.
Two examples:

A. The
cell showing the linkage
between the NGO and the VDCs could be developed into a new more
detailed matrix,
where the left column listed the NGO Community Development Workers
(CDW) and the top row listed the various VDCs the NGO was working with.
The cell values could indicate the
percentage of time each CDW expected to spend working with each VDC.
Many of the CDWs might be expected to work with multiple
villages, but addressing different development tasks.

B. The cell showing the NGO linked to the NGO could be
developed into a new more detailed matrix, where the left column and
the top row both listed
the NGO staff (CDWs and other staff). Cell
values could describe the relationships between all the staff.
Cell values could code types of working relationships, or percentages
of the row actor's time that will be spent working with each other
actor.

Note:
Not all the relationships in the original matrix above would need to be
expanded in detail, in this way. Such a process would be time consuming
and unnecessary. The choice of which cells to expand into new more
detailed matrices should be a
strategic choice, reflecting a sense of what are the most
important relationships within the whole model, which need more
detailed planning and description.

A
matrix that is used to represent a
whole set
of more detailed matrices, has been called a "meta-matrix" by Krackhardt
and Carley, 1998 This was in the context of
developing quite complex computerised models of organisational
processes, which are not relevant to task being addressed here by this
webpage. The most immediate use of a meta-matrix is as a
means of developing a simple network model of a development
project to start with, then deciding where to selectively develop the
model in more detail.

It
is
also possible to scale up as well as down, using
the same technique, and place the project in a wider context. The
network matrix shown above could be seen as one cell in a larger
matrix, such as one showing a range of projects in a given country and
their interactions with each other. Use this way,a meta-matrix can
provide a
visible set of links between network models developed at different
scales and locations. Contra the Logical Framework, network models are
scalable!

Over time:
Start with the actors who will be involved at the beginning, and map
out the expected relationships between them. Then add in the new actors
that
will get involved, each x period of time. And map the relationships
they will have with the existing actors. There are two options here, to
develop: (a) A cumulative
model, that shows all actors and
relationships
that have existed up to the final period of concern, (b) A consecutive
model, in the
form of a series of "slides", showing pictures of the network
at
different points in time. This could require change or removal of old
relationships and actors, as well as addition of new actors and
relationships.

Opportunistically:
Document the relationships that exist at this moment according to
whatever data is
available. Then ask the participants (a) How this network structure
relates to their original plan, of what they
intended to see happen when they first got involved, (b) How well does
the network structure represent what is actually happening and the
moment, (c) How they
expect this network structure to change by the end of x period.
Make sure they can
comment not only the structure of the relationships that are mapped,
but on alternative types of relationships (not shown in the network
diagram) that might be more important to them, and which should be
mapped. Many of the example networks shown in section 12
below have been opportunistically developed.

8.Describing
relationships within a network matrix or network diagram (return to Contents)

The matrix that has been shown above has a very simple
description of
the relationships involved. It simply states whether a relationship
exists or not, not more. For the purpose of project planning and
evaluation a more detailed description would be needed. In Social
Network Analysis cells in a matrix can be used to describe many aspects
of relationships:

Existence
of a relationship: Described by using a 1
or 0 in a matrix (as above), or the presence or absence of a link in a
network diagram

Type
of relationship: Described by using
numbers, such as 1, 2, 3,... to indicate the presence of different
types of relationships that have been pre-coded.

Frequency
of interaction: Described by using
numbers to indicate frequency per period or in total. Or by indicating
the relative proportion of an actor's time spent on each relationship.

Value
of the relationship: Described by
using numbers to signify a rating or ranking of the relative value of
different relationships

Sequence
of the relationships: Described by using
numbers (e.g. 1, 2, 3) to representing a sequence of events over time,
or dates representing actual times

Details
of a relationship: In small matrices the
cells can contain text descriptions of the relationship

The same
information
can be shown in network diagrams by colour and size coding the links
between the actors

The
choice of
what aspects of a relationship to map is a strategic choice, of one
amongts many, that needs to be informed by a theory, or view, of what
is most
important in the network being modeled

Cross
and Parker (2004)
have focused on information flows within organisations, and how to
inquire about them. Consistent with
the thrust of this webpage, they have commented that "We think that one
trend in network analysis will be towards mapping different,
theoretically important dimensions of relationships". Some
of the
candidate dimensions of relationships they have suggested need
exploration are:

Who
do people contact for task
purposes - people who provide information , resources or
diretcion that helps us get work done

Who
do people contact for career
development (learning) - people who give feedback that is
helpful for our career development

Who do people contact for career
support (political support) - people in influential
positions who are advocates and provide political support

Who do
people
contact for sense
making -
people who help make sense of
rumours, events, or gossip

Who do
people
contact forpersonal
support -
people who help us cope with and recover from troubling situations at
work or personal dilemmas.

Who do people
contact for purpose - people who make use feel that
what we do at work matters, that our work has meaning

9. Using one
and two mode networks

There are two main kinds of matrices that can be
used to
describe networks:

One-mode
(symmetric or adjacency)
matrices: For example a matrix showing how 10 NGOs were
related to each other. The same list of NGOs would be shown down the
left column of a matrix, and across the top row. The matrix shown above
is a one-mode matrix. The same set of actors are shown in the top row
and left column. The matrix shown in section 6 above is a one-mode
matrix.

Two-mode
(asymmmetric or affiliation)
matrices:
For example, a matrix showing which of the 20 objectives in a
government poverty reduction strategy that each of the 10 NGOs was
addressing through its research and advocacy work. Here the NGOs would
be shown down the left column of a matrix, and the policy objectives
would be shown across the top row. Each cell could contain a number
signifying the relative importance of a given policy objective to a
given NGO. Two-mode networks can be more interesting, and
useful
as development interventions, because participants' knowledge of the
whole network is usually less complete than in one-mode networks.
Constructing and then then sharing the network model can be a
development intervention in itself.

Within the matrix version of the Logical Framework
shown in
section 6 above, we can see a number of potential one-mode and
two-mode matrices. The cells in the diagonal (upper left to
bottom right) show relationships between the same kinds of actors
(NGOs with NGOs, Donors with Donors, VDCs with VDCs, etc). As noted
above, each of these could be developed into a matrix of its own. These
would be one mode-matrices, because the same actors are listed on both
sides of the matrix. All the other individual cells could also be
developed into matrices, but these would be two-mode matrices, because
the actors on the side of the matrix would be different from those
listed across the top.

"Multiplex relationships"
is a term to describe where two actors might have multiple kinds of
relationships with each other. For example, in aid agencies people will
be connected
through formal organisational structures, and informal through
friendship and other ties. Multiplex relationships can be shown in two
mode matrix format, that has a one-actor x multiple-other-actors
structure. Each row will then show a particular kind of relationship
that actor has with all the other actors. For example, an NGO may
produce many kinds of information products, for a range of other
organisations. The left column could list those products, and the top
row could list the organisations using them, or expected to use them.
Cell entries in each row could indicate whether a given actor uses, or
is excpected to use a given product. Multiplex relationships
can
also be shown in network diagrams by color coding different types of
linkages.

9.
Summarising the data available within a network matrix (return to Contents)There is a third strand of network analysis,
in
addiiton to matrices and diagrams. These are mathematical measures of
the structure of networks. Many are far too complex to be of day to day
use in the development of network models of development
projects. There are however some simple measures which can be
useful, and which are outlined here.

1. Using
summary rows and columnsA matrix full of
numbers can be daunting, especially
a large matrix with many actors. How do you make sense of all that
data? One simple way forward is to make use of a summary row
(at the bottom), and a summary column (to the right). Here
below is another version of the matrix already shown above. The cells
in the summary column count the number of links each row actor has with
all the column actors. The cells in the summary row count the number of
links each column actor has from all the row actors.

PS: Because each relationship in this matrix is a two-way relationship,
the row and column totals are the same. But if the matrix described
funding relationships between the actors (a one way relationship) this
would not be the case.

2. The same matrix could include valued relationships with each cell,
describing the relative importance of the column actor to the row
actor. In this case we can use two types of summary rows. These are
shown in the matrix below. 1 = highest priority, 5 = lowest.

Here the links to the VDC are the most important links , and
the links to the Others (Output Assumptions) are the least important.
Remember that in this example, low rank numbers = high importance.

In network diagram versions of the same matrix, it would be common to
show the relative importance of the different links, by varying the
thickness of lines signifying a link. And the size of the node
representing each actor could vary according to the number of links it
has with others.

3.
Introducing
actor attributes, to weight the importance of relationships

We can make the picture more detailed (and more realistic), by
introducing another category of information into the picture. So far
all the data inside the matrix describe the relationships
between the actors involved. This is the traditional focus of social
network analysis. However, we can also introduce some data
about the attributes
of each of the actors involved. These attributes could be the
size of the group (organisation staff or group members), the resources
they have available, their relative status or importance,
their willingness
to become involved in a project, or any other measure relevant to the
expected success of the project. These attributes may make a
big difference to what happens within a given relationship (as
specified within the matrix).

The matrix below includes some imaginary attributes of each of the
actors, on the far left. The cells combine these with the relationship
values taken from the matrix above. The second summary row
then shows the combined "effects" of these weighted
relationships on each column actor.

The introduction of weightings has changed the picture, with the
linkages to "Other Villagers" now having the highest average
importance, compared to the linkages to the VDCs which were more
important previously. Again, remember that in this example,
low rank numbers = high importance, and vice versa!

In network models the equivalent is a process of expected influence in
the form of: Actor + Relationship + Actor +
Relationship + Actor... [but one involving multiple interacting actors,
not a simple linear sequence]

Giving actors numerically valued attributes, and combining them with
relationship values, allows us to convert this text description into a
quantified description. If the same actors are also shown in other
matrices, with links to other actors, the effects of their complex
interactions can be traced over longer distances.

In the simple example above, the actor attributes were judgements about
their relative importance. Other significant attributes could be their
size (in staff or budget), if they are organisations.

There are a number of measures of network structure that have been
developed within the field of Social Network Analysis that may be
helpful when analysing the network structure of development projects.
Some of these relate to the position of individuals in a network, and
some relate to the structure of the network as a whole. Some of these
can be directly observed in network diagrams, others can be identified
using social network analysis software described further below.

The
position of individuals within networks

Centrality:
There are different ways of describing how central an actor
is in a network, and being central may be a good or a bad thing.

A simple measure of centrality is called Degree Centrality
This is the number of links an actor has with other actors. In the
matrix above, the VDC has the highest Degree Centrality (4) and
the Donor has the lowest (1).

There are two kinds of degree centrality: In-Degree and Out-Degree. These
are shown in the summary row (# of links in to an actor) and
in the summary column (# of links out
from an actor), respectively. These measures are easy to
calculate, and can be shown in summary rows under a matrix (using the
Count function). They can be very important where the network
represents (expected or actual) influence
relationships. An actor that influences many others is likely to be
seen as powerful. On the other hand, an actor that is influenced by
many other actors, is likely to be seen as much less so. Most
actors in networks will have varying combinations of In-Degree and
Out-Degree centrality. The same measure is also relevant when
looking at networks of project activities, and their immediate effects
on peoples' lives. Project activities that influence many other
activities may need careful planning. Changes in peoples lives that are
influenced by many project activities will warrant more intensive
monitoring.

Related to In-Degree and Out-Degree is the extent to which links between
actors are reciprocated. This is important to attend to
when information on linkages is collected from the actors themselves.
One actor may say they are working with another, but that second actor
may not report working with the first. This may be a simple measurement
error or it may be symptomatic of the relative status of the two
actors, with the lower status actor wanting to report a relationship
with a higher status actor, but not vice-versa. In some
circumstances it may be worth analysing the extent to which each actors
outgoing links are reciprocated by incoming links

Betweenness
Centrality: An actor might not have many connections with
others (i.e. low Degree Centrality), but those they have might still be
very important. Betweenness Centrality describes the extent
to which an actor is situated between two groups, and is a
necessary route between those groups. Such people can act as
mediators between those two groups, or they can become unintentional
bottlenecks, or they can be deliberate obstacles to communications
between those two groups. Which of these roles they take on
will, in part at least, depend on their relative power and status,
compared to the two groups they are linking. It is not easy to show
Betweenness Centrality in a summary columnn of a network matrix.
However, in relatively small and /or simple networks it is often
possible to identify which actors have high Betweenesss Centrality by
looking at network diagrams. In the network diagram in section 5 above
the Village development Committee has the highest Betweeness
Centrality. For larger and more complex networks,
social network analysis software can be used to identify Betweenness
Centrality, and many other measures, for all the actors involved. Actors
with high Betweeness Centrality clearly have the potential to have a
major influence on what happens in a development project, and therefore
need to be identified and monitored.

Closeness
Centrality
is a measure of the average distance between an actor and all
other actors in the network. These actors are likely to be most "in the
know" about what is happening. Finding these actors and making use of
them through M&E activities would make sense.

Peripheral
actors:
These are the converse of the above. They may have few
connections with other actors, not in any key brokerage roles, and at a
higher average distance to others. But they are worth knowing about.
They may be more independent minded, because they are not
part of a group which has self-reinforcing beliefs. They may have links
with other networks, which take up more of their time, but which could
provide useful new knowedge and resources to the network being
analysed. Or they may be truly marginal, unconnected to other
networks, and at risk of neglect by the actors within the project
network

Note: It is
important to remember a general point: that all network models are incomplete.
Many if not all the actors in the network of concern will have
connections with others outside the network. And even within
this network, there will be multiple other kinds of
relationships between the actors. A network model will always
be a purposeful simplification of reality.

Structural
Equivalence:
Two or
more actors who have the same structure of relationships with other
actors are described as being "structurally equivalent". For example,
two
northern donor NGOs may have relationships with
a very smilar set of recipient southern NGOs. In this setting two
different questions could be asked. Firstly, in what way can each
northern NGO be differentiated from the other, when
they are funding the same set of southern NGOs? This is all about
differentiating roles, and identifying niches. For example, one might
focus more on
funding support, and the other on technical support.
Secondly, how
these northern NGOs work together to maximise the value of
their support to their common southern NGOs. This is all about
coordination and harmonisation.

Node 7 has the highest Degree Centrality
Node 8 has the highest Betweenness Centrality
Nodes 4 and 5 have the highest Closeness Centrality
Node 10 is the most peripheral, having the least connections of all
Nodes 4 and 5 are "structurally equivalent"

The
structure of networks

Network Density:
This is a measure of how inter-connected a network is. A
network where all the actors are connected to all the other actors is
said to have a density of 1.0 This is the maximum possible. In the
example matrix in section 5 above, that network has a density of
0.55 (that is, there are 20 of the 36 possible links).
Such calculations can be easily built into spreadsheet
versions of network matrices.

As with other measures above, network density may be good or bad. A
high density network will be less vulnerable to the breakdown of any of
the links between the actors, but this will be at a cost. All the
actors will be having to manage multiple relationships, and they may
not do this as well as they might if managing a smaller number. Within
highly hierarchical organisations the density of formal linkages will
be quite low, but in organisations using ad hoc teams or a form of
matrix management, density with be relatively high.

When developing network models of development projects it is important
not to design networks that are too dense. If everything seems to be
connected to everything else, then it is hard to see what are the
important linkages that are central to the project design. There are
two ways around this problem. One is to omit the least important
linkages, as has been done in the matrix above. The other is to put a
value on the relative importance of each linkage, so it is possible to
selectively focus in on the most important linkages. Doing both is even
better.

Clusters:
Many networks will display some form of clustering, a cluster being a
groups of actors with many inter-connections between each other, but
few with others. It is sometimes possible to see clusters in network
matrices, if the contents have been sorted by row and column
beforehand. And by visual inspection of network diagrams. But with
larger / more complex networks it is helpful to use statistical
functions in social network analysis software, to identify clusters at
different scales of connectedness, and the overall degree of
clustering.

The identification of clusters in development projects is important.
This aspect of a network structure is likely influence the flow of
information of concern to a project. Information will normally flow
better within clusters than between clusters. And conversely,
the
relative availability of information of concern to a project, from what
seem to
be different groups actors, may tell us about the overall structure of
their relationships. Important
Caveat:
The significance of all the measures of network position and structure
introduced above has to be interpreted in the light of the intentions of the
actors involved. Either of the person who has developed a network model
as a plan. Or, the actors in the network, if the network has been
developed as a description of current relationships. None of
the network attributes introduced above are by definition good, or bad.

Steve
Borgatti (in
a technical paper) has made some useful distinctions about
types of processes that can taken place within networks, according to:

How
things move within any relationship:

By
replication (like information), or transfer (like physical
goods)

And if by replication: serially (in
one-to-one
discussions) or in parallel (like email, or public announcements)

How
things move through networks of relationships

One way only, never retracing its
steps. For
example, used goods, gossip and diseases (where immunity can be aquired)

Both ways, like money.

These
are useful distinctions, because they have consequences for how we
interpret the significance of a particular network structure.
Things that can spread by parrallel replication that can
retrace their steps are likely to be least constrained by network
structure. And vice versa, things that have to be moved, and
which cant retrace their steps, will be most constrained by network
structure. Humanitarian emergencies are more likely to involve
movement of physical goods than development programs, and in this
respect more likely to to be constrained by network structure. But the
introduction of cash grants to victims of disasters can be one way
around those constraints.

In development projects it may be more useful to think about different
kinds of information flow, and how they are affected by network
structures.

Private information:
being information that can only flow from person to person (serially).
The content of "private" information that a person can report be more
symptomatic of their position in a network than public
information, which can spread in by more mass means.

Public
information is less constrained. Allowing information to
be put in the public domain is one way of empowering people, because
they will no longer be as dependent on network structures for access to
that information

Attitudes:
Spread not through one-off contacts, but through repeated contacts. As
such they may be symptomatic of the duration or strength of
relationships. Or the absence of contacts, if we know they are
ill-founded.

News:
Like other types of information, news can be replicated and spread in
parallel. But it may move through some parts of a network faster than
other parts. Where news first becomes available may be
symptomatic of the structure of the sorrounding network.

Money:
Is partly like information and partly like a good. It can be replicated
(digitally) but under strict constraints (conservation of quantity). It
can be split (to a finite degree) and re-combined as it is
transferred through networks of actors. Budgets are means to capture
what is received from whom and what is passed on to whom. But the idea
of "fungibility" captures the idea that we cannot trace what happens to
individual units of money, when they are received by one actor from
different sources, and then sent off to different receipients. Budget
transparency is all about making the pathways that money follows more
visible. That requires readable budgets, but also budgets that have
visible onward connections, to people (or organisations), their
activities and objectives.

"....when one takes into account timing of
the flow of things through a
network, ones perspective on key structural features of the network
fundamentally changes. For example, the most central node in a network,
e.g., as measured by betweenness, can look peripheral if one takes into
account the timing of flow. If the most central node gets information
slowly, information will flow around that node. A dynamic picture of
flow in a network thus can produce a fundamentally different
understanding of the structure of the network than a static picture. (I
should note that a dynamic picture does not mean necessarily that the
network is changing—just that there may be a natural sequence
in
communication, which may be a long standing structural feature of the
network. That is, there is a difference between saying that networks
are dynamic and that networks evolve over time.)"

12.
Analysed examples of developed network models (return
to Contents)

Most
of the examples here are not network models of project intentions
that have been deliberately developed by project managers or designers.
Rather they
are network representations of aspects of development
projects, developed opportunistically, when useful data became
available. As such it is useful to ask two types of questions about
these models:

(a) To what
extent are events actually happening as
described or
implied by these network models?
(b) To what extent to these models represent what was expected to
happen?

Please note that the example page links are still being developed.
Links in bold
with *
are now active. The list of examples is not in an especially meaningful
order

Networks
within organisations

*Strategic
objectives and organisational structure. Three examples are
shown
of administrative
units within organisations and how they are linked to each other by
their shared
strategic objectives (or not). Tow questions: 1. Does this
organisational structure
reflect how strategic objectives are meant to relate to each
other? 2. Does information flow between these linked sections
because they are talking about their shared
strategic objectives?
Budgets and their relationships to project activities. The
structure of budget categories and sub-categories usually has its own
history, independent and predating the planning of activities in a new
project. There are also often real constraints on what changes can be
made to budget categories to make them have a better "fit" with
categories used to describe proposed activities in a new project. The
alternative is to construct a budget lines x project activities (or
outputs) matrix. That is, a network model of how these two sets of
entities are expected to relate. One benefit is that the total cost of
each output becomes visible, through the use of summary rows (as
described above).

Communications
strategies and actual practice

The
interaction of communications products with audiences, and
audiences with each other
An organisation may produce multiple types of communications
products. These will be expected to be used by by different
audiences,
in different combinations. These audiences may be expected to
subsequently interact with each other, in varying ways. These
relationships can be shown
in a series of linked matrices (products x audiences, audiences x
audiences)

The same
organisation may also have plans for
how to engage their audiences with some communications products
initially, then with
other products later on. A network
diagram can developed to show how
users will be expected to move from one product to another. Actual use
of those products can then be monitored to see what is
actually
happen, and how it fits with initial plans.

*Networks of
participants and events. An NGO organises a series
of workshops, over a period of years. Quite a few people attend a
number of these workshops. They are potentially
connected by their co-participation. Overlaps in groups of participants
can be intentional as well as fortuitious. Creating and reinforcing
relationships may be an important outcome of the workshop series, as
well as the more fleeting exchange of information during a particular
workshop. New section
(27/04/06):
A research funding mechanism in Vietnam has organised a series of
workshops to publicise its partners' research findings. These events
are linked by co-participants. A series of useful questions can be
asked about the structure of the network that results.

Funding relationships

*A research funder
and its network of grantees.
(PETRRA in Bangladesh). This is a network of contractual relationships
between five different types of organisations within Bangladesh and
beyond. Interesting questions can be asked about the
project's original intentions and how its longer term impact could be
assessed. It is also possible to develop two levels of models, a simple
one showing relationships between types
of organisations, and a more
detailed model showing relationships between specific organisations
that belong to these types.

Bangladesh
NGOs and their multiple northern donors.
A survey of Bangladeshi NGOs in 1992 showed that they were connected to
each other via an overlapping set of funding relationships with
northern donors. This was not a planned development, though there may
have been local coordination activity amongst some donors. It
is
clear that some donors have very similar
sets of linkages
with NGOs, which raises two questions. (a) What sort of differences
exist between them, that could justify their separate existence, but
shared relationships with NGOs? (i.e.
what is their niche?), (b) How can they cooperate, to maximise the
value of their support, and minimise the costs to NGOs of having to
deal with two separate donors?

Relationships
funded NGOs have with others.
G-Rap has provided core funding for 12 Ghanaian NGOs, all of
whom
are engaged in some form of research and advocacy activities. They have
some working relationships with each other, and with others. Some these
other relationships they share, and some are unique to each NGO. These
relationships include other NGOs, community based organisations,
central and local government bodies, donors, and others. This complex
network can be analysed using the idea of social capital as involving
bonding and bridging capital. The former is all abou the
strength
of links between the funded RAOs. The second is all about the unique
connections each NGO has with other parties, which might be valued by
the other NGOs.

*Networks of organisations and
projects. Research projects can
be connected
by the overlapping participation of different institutions. These
provide potential channels whereby ideas and practices from one
organisation may influence another. As a network they provide a number
of potential impact pathways that might be realised in the relatively
short term, versus the impact on a wider range of more distantly
connected organisations. Which of these pathways is most desirable, and
which is most likely to be realised?

Relationships
arising via shared objectives

*INGO networks in Vietnam.
International NGOs can be potentially linked together by working in the
same locations, or in the same sector. The VUFO-
NGO Resource Centre
in Vietnam produces an Annual INGO Directory. The Directory
includes two extensive cross-tabulations, showing which INGOs are
working in which provinces and in which sectors. These can be used to
generate two-mode networks.

Global
networks of projects linked to global program objectives :
The CIAT Water and
Food Challenge Program has 33+ projects, in 15 river basins, in
x
countries, on three continents, with more than x participating
institutions. Where is the structure and coherence? Is the
structure we can see what was intended? And what are the expected
consequences of this structure? Where should information and influence
be flowing?

*Networks
of donors and policy objectives: A network
perspective on donor
harmonisation in Ghana, in the form of a matrix showing what
donors will be providing funding support to what Growth and Poverty
Reduction Strategy policy objectives. Qustions can be asked
about who needs to be talking to whom, about what?
And if you are an NGO wanting to do advocacy work on poverty related
policies, what are the implications for who you work with?

Networks
over time

Networks
of influence over time: Fisheries research in
Australia. Research projects can influence multiple other
research projects, in the short term and in the longer term. The
result: a complex genealogy, a network spreading through time
rather than a simple branching family tree. Who seems to have been the
most influential over the longer term may be relevant to analysis of
the long term impact of the research funding mechanism.

Document networks

Policy
document networks.
Development policy documents often include lists of
indicators, and overlaps between these indicators can sometimes be
easily identified. The 2003 M&E Plan for the Ghanaian Poverty
Reduction Strategy included a table showing which indicators were being
used on three related policy documents (HIPC, GPRS, MDG). Treated as a
two-mode matrix the results can be converted to a network diagram which
readily shows in some detail how the policy documents are related.

Causal
networks Many large projects
have multiple objectives. Many of these objectives can be expected to
feed into each other, with the achievements of one contributing to the
achievement of another, or multiple other objectives. This
means there will not be a simple one-to-one correspondence between the
amount invested in an objective and the degree of its achievement. Some
objectives may require additional investment, others less than
expected. The network of causal linkages will also have implications
for where M&E efforts should be concentrated.

All network models
are
incomplete, both by necessity and by intention. They show particular
relationships between a set of actors, but not other kinds of
relationships that are seen as less important. They show relationships
between a specific set of actors of concern, but not their
relationships with the many others who sorround them. They show
relationships within a given period of time, but not the relationships
that preceded them, or which may follow later.

Where network
models are being
used to represent the design and / or achievements of a project it can
be of value to ask questions about this wider context.. For example:

Re wider networks

What
are the most important wider connections that each actor has
with other actors outside the network?

Which of these may
provide the network with
opportunities?

And which of these may function as significant constraints?

PS: The focus on
each actor's unique connections with others outside the
immediate network is a
way of describing their "bridging" social capital

Re
pre-existing and subsequent relationships

Which of these
relationships pre-existed the project, and which do not?

Which of the relationships in
the network model are expected to continue to
exist after the project finishes?

Records
of participation: people and organisations who were
participants in various project "outputs": workshops,
training events, mailing lists, websites, etc

Reports:

Lists of
cross-references to other documents and
other authors

Tables of
objectives, sub-objectives and
indicators

Tables of
data in annexes (where more
data,
in more detail, can be allowed)

Online
sources

Websites:
Logs of visitor activities, help by all
hosts of websites.

Google
searches: Websites, and documents,
deliberately linked, and linked by shared use of same key words

Special
purpose surveys

Face to
face.

In
workshops

Online

Content
analysis of documents

Coding of
content into different categories of
actors, events, etc, then treating their co-occurence in a
document, or section of a document as a network
linkage...

Survey methods
[to
be developed]

15. Using
Social Network Analysis software (return to Contents)There are three specialist software packages that can
be used, as
listed below. But note that for small networks, all that is needed is
an Excel spreadsheet (for matrices) and the Draw function in Excel (for
network diagrams)

UCINET,
at http://www.analytictech.com/ucinet.htm
This includes the NetDraw program for producing network diagrams, using
data input into UCINET.Good
feature: You can easily cut and past matrices of data into
a spreadsheet within UCINET. And there is an online Users
Group that you can join, and get help from

Caveat:
The main problem with all three is that
there are lots of
"Bells and Whistles" in these programs that you need to ignore at the
beginning, while you are still struggling to learn how to import and
export data, and to create and manipulate network diagrams. Be patient
and persistent.

Because social network data is
increasingly amenable
to analysis using social network analysis software there is a risk that
analysis of networks will be reduced to a 21st century version of
"number crunching". If the data is there, and the software is there,
why not crunch the numbers?

The answer is that this will only take us so far. Analysis
needs to be informed by a theory of what is supposed to be happening.
While our own theories may be interesting and relevant, the theories of
the actual participants in these networks will probably be even more
so. This is especially the case in development projects,
where
there will usually be some intention to seek change in the behavior of
at least some of the actors in the network.

Social
network analysis has come from a fairly academic
background, so there is still plenty of room for innovation relating to
practical applications. Especially in participatory approaches to the
description, analysis and planning of networks. If you have any ideas,
have already tested some methods, or know of others who have, please
use the Participatory
Network Models wiki page to share these with others. Options People can be involved in the development of network
models in different ways:

Planning the changes needed,
in the form
of a network model

Describing what the current
situation is
(before, during or after plans are implemented), in the form of a
network description

Making predictions of the
expected
results of a survey of network structure, shortly before they
are analysed and presented

People
can participate via the use of various tools

Individually,
by being survey respondents and providing data on their place in
networks. This is the most limited form of participation but the most
widely used.

But prediction questions
could be
built in to such
surveys, about the likely response of others. For example: After
answering a particular question about teir relationships with others,
then ask "Who is likely to name you in response to this question? I
think this takes us in the direction of cognitive social networks,
tracking what people know about others' views of networks (Readers:
Correct me if I am wrong)

Collectively,
by discussing and analysing data as they provide it, after it has been
analysed and fed back.

Both matrices and network
diagrams can
be used in workshop settings and
can be the focus of intense and lively debate

I have outlined my own
experience
with both of these in the wiki
mentioned above

In outdoor situations Venn
diagrams
can be drawn
on the ground, in a small group setting, to capture relationships
between groups of people.

Dynamically,
by involving people in what are called "social simulations" of
networks, whereby they take the part of a given actor (person,
organisation or group) in a network, and make a relationship with
another in that network, to pursue an objective. This process is then
repeated over a number of iterations, or generations, allowing people's
subsequent choices to be informed by their knowledge of the choices
made by other actors in the previous iteration.

A "Getting to know people"
exercise
example: Round
1: individual participants pick another participant they dont know, and
find out about them. They also find out who else they know in the room,
and what they know about them (perhaps some pre-specified information).
Round 2: They pick another actor, and find about them. They
also find out who else they know in the room, and what they know about
them. Round 3. As above, and so on with subsequent rounds until the
first person claims they have all the (relevant information) about all
participants. Or until a defined number of rounds are completed, at
which stage you map who knows whom, and who they do not know.
As
well as identifying who is most known, this process may also help
identify those who are unknown, and therefore in most need to be known!

17.
Available
data sets This
section will
contain a
list of links to files of data on a range of networks. In most cases
they will relate to the network model examples accessible via section
12 above

The purpose is to
involve more people in the analysis of this data. A: To give people
data to use for practice purposes, to develop their skills in network
analysis, and B:
To bring more brains to bear on the same data, and perhaps generate
some more creative and or complete analyses, building on whatever has
been done so far. So, if you down load this data, and use it
for
network analysis, please do so with the intent of sharing the results
of your analyses with Rick Davies (the author of this web page) and
others who are on the Network Evaluation email list

The data sets that
will be listed here are (provisonally):

30
NGOs in Bangladesh interviewed in 1992 x the 50+ donors they reported
as funding them,
with some categorisation of their status as donors (primary
versus other, years as a donor). Click here for the example analysis of
this data on this website.

A 13 year time series on international NGOs in Bangladesh
x 30+ sectors they were working in each year, and international NGOs in Bangladesh
x 60+ privinces they were working in each year.
Click here for the example analysis of this data on this website..

With their emphasis on structures of
relationships,
network models may
seem to biased towards deterministic view of human activities, and may
appear to neglect the significance of human agency, the
ability of individuals to make a differences. This should not be the
case, if network models are used wisely.

Network structures can not only influence human
actions (both by
constraining and enabling), but they can also be the
outcome
of human actions. In other words, we can see network structures as
independent variables, and as dependent variables. Both
perspectives are important. At the planning stage of a development
project we may need to focus more on how existing network structures
can enable and constrain proposed developments. At the end of a
project, an evaluation may need to attend to changes that have arisen
in the structure of relationships between project stakeholders, and
their causes. And looking further forward, beyond the project lifespan,
the evaluators may also need to ask how sustainable various
relationships will be, and what factors will affect their
sustainability.

Network structures only have significance in as much
as they are seen
and interpreted by the actors in those networks. A link between two
actors will be of little significance, if for some reason those two
actors (and others) have forgotten about the link.
Differences in their perception of the value of that link will have
consequences, regardless of how an independent observer might
assess the link. As already argued above, we not only need
data on what relationships seem to exist, but also the actors's views
of those relationships: their relative significance, and how
they relate to actors' prior plans and intentions. Narrative
methods, such as Most
Significant Change (MSC) monitoring may be a useful means of
collecting and analysing such information.

One characteristic of complexity is
unpredictability. One source of
unpreditability is feedback. Beautifull and unexpected fractals can be
produced when the results of a calculation are fed back as an input
into the next iteration of that calculation. In Conway's artificial
life program (Life),
a diversity of life forms emerges as a population
of "live" cells follow a simple set of rules about how to respond to
their immediate environment. And where in the process their
environment is
changed by the results of following those rules.

Complex systems are almost by definition dynamic, concerned with change
over time. In contrast, network models have frequently been criticised
as being overly static, focused on the state of a social or other
system at a point in time. Network models can however
generate
dynamic and
complex processes, if the "outputs" generated by a network model are
fed
back
to inform the "inputs" of the next "generation" of this model. The
inputs in a network model are the attributes of the actors and the
structure of the connections they have with each other (both the
existence of specific connections, and their strengths, as given in the
cell values). The
outputs can be seen in the summary rows and summary columns
shown
in
the example in section 9 above. These can be used to
re-adjust
the input values in the next version of the network model.

This updating process can happen in in two ways: (a)
through a social process, such as a workshop. (b) mathematically, by
creating
a formula that uses the output values to recalculate the input values
(especially the actor attributes).

In a workshop setting participants can complete a
network matrix, by
identifying and agreeing on appropriate cell values. These can be
inserted in an matrix presented in an Excel file, projected onto a
screen visible to all. The summary row can be constructed to
automatically generate summary values, as soon as the cell values are
entered on
the screen. The facilitator can then ask participants to reflect on the
summary values, and their acceptability, relative to their views up to
then. If the values are not acceptable, participants then have three
choices, which would cause changes in these values:
(a) Change the characteristics of the actors in the left hand column
(b) Change the characteristics of the actors in the top row
(c) Change the linkages between them, by changing the cell values
within the matrix

Complex systems can exhibit different kinds of behavior, according
to the structure of the connections between the actors
involved.
They can (a) be chaotic (i.e. summary values will vary
unpredictably
over a long series of iterations), (b) tend towards a stable
state (i.e. summary values will stablise, and not change any further,
despite further iterations), or (c) exhibit complex behavior (where
particular summary values appear, disappear, and re-appear in cycles).

Most organisations using network models will be interested in models
that will generate stable sets of values, and seek to avoid those which
generate chaotic or complex behavior. In workshop settings there is
usually limited time available to develop network models, and then run
them through one or more iterations. So participants are likely to have
an interest in coming to agreement about the acceptability of summary
values relatively quickly. Where outputs are linked back to inputs
mathematically, through the use of formula in a spreadsheet, there is
no such "inertia", and reaching a stable state may be more difficult.
In these circumstances simplifying the structure of the linkages may
help. Evidence seems to suggests that networks with high degrees of
connectivity are more likely to show chaotic behavior [Readers: Please
correct this statement if it is wrong]

Example
of stabilisation of attributes values for actors,
when matrix summary values are fed back as inputs

Postscript: What was intersting about this simulation
was that the initial values that were generated through the first
iteration of the mdeol were quite different to what the final values
settled down to, by the ninth iteration. See actors 2, 3, 4
above, especially. This suggests that running iterated
versions
of causal network models may be worthwhile. The Excel spreadsheet that
was used to generate these values can be found here.

And Valdis Krebs'
more application oriented web pages at http://www.orgnet.com/ See the 24 examples. Every one
of them is interesting.

FootnotesBoundary
partners: "Those individuals, groups,
and organisations with whom the programme interacts directly and with
whom the programme anticipates opportunities for influence" A term
borrowed from
Outcome Mapping